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csvlogger

csvlogger

Implementation of the CSVLogger callback for TensorFlow.

Classes

CSVLogger 

CSVLogger(experiment_context, filename='train_logs.csv')

Bases: Callback

Callback that streams epoch results to a CSV file.

Supports all values that can be represented as a string, including 1D iterables such as np.ndarray.

Example
csv_logger = CSVLogger(exp_context=exp_context)
model.fit(X_train, Y_train, callbacks=[csv_logger])
Arguments
exp_context: An instance of ExperimentContext which is used to write csv files

Initialize CSVLogger with a given Context and csv filename.

Supports all values that can be represented as a string, including 1D iterables such as np.ndarray.

Example
csv_logger = CSVLogger(exp_context=exp_context)
model.fit(X_train, Y_train, callbacks=[csv_logger])
Arguments
exp_context: An instance of ExperimentContext which is used to write csv files
separator: string used to separate elements in the CSV file.
Source code in niceml/dlframeworks/keras/callbacks/csvlogger.py 
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def __init__(
    self, experiment_context: ExperimentContext, filename: str = "train_logs.csv"
):
    """Initialize CSVLogger with a given Context and csv filename.

    Supports all values that can be represented as a string,
    including 1D iterables such as np.ndarray.

    # Example
    ```python
    csv_logger = CSVLogger(exp_context=exp_context)
    model.fit(X_train, Y_train, callbacks=[csv_logger])
    ```

    # Arguments
        exp_context: An instance of ExperimentContext which is used to write csv files
        separator: string used to separate elements in the CSV file.
    """

    self.filename = filename
    self.keys = None
    self.data = None
    self.experiment_context = experiment_context
    super().__init__()
Functions
flush 
flush()

The flush function is called when the training is finished or at the end of an epoch. It writes the data to a csv file in the data directory of your experiment.

Source code in niceml/dlframeworks/keras/callbacks/csvlogger.py 
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def flush(self):
    """
    The flush function is called when the training is finished
    or at the end of an epoch. It writes the data to a csv file in the
    data directory of your experiment.
    """
    if self.experiment_context is not None:
        self.experiment_context.write_csv(self.data, data_path=self.filename)
on_epoch_end 
on_epoch_end(epoch, logs=None)

The on_epoch_end function is called at the end of every epoch. It writes the logs to a CSV file.

Parameters:

  • epoch –

    Current epoch

  • logs –

    The logs of the current epoch to be written to the csv file.

Source code in niceml/dlframeworks/keras/callbacks/csvlogger.py 
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def on_epoch_end(self, epoch, logs=None):
    """
    The on_epoch_end function is called at the end of every epoch.
    It writes the logs to a CSV file.

    Args:
        epoch: Current epoch
        logs: The logs of the current epoch to be written to the csv file.
    """
    logs = logs or {}

    if self.keys is None:
        self.keys = sorted(logs.keys())

    if self.data is None:
        self.data = pd.DataFrame(columns=["epoch"] + self.keys)

    row_dict = {"epoch": epoch + 1}
    row_dict.update({k: np.round(v, 6) for k, v in logs.items()})
    self.data = pd.concat([self.data, pd.DataFrame([row_dict])], ignore_index=True)
    self.flush()
on_train_end 
on_train_end(logs=None)

The on_train_end function is called at the end of training. In this case it writes the logs of the current training to a csv file.

Parameters:

  • logs –

    The logs of the current training. Ignored here because the logs are stored in the object (self.keys, self.data).

Source code in niceml/dlframeworks/keras/callbacks/csvlogger.py 
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def on_train_end(self, logs=None):
    """
    The on_train_end function is called at the end of training.
    In this case it writes the logs of the current training to a csv file.

    Args:
        logs: The logs of the current training. Ignored here because the logs
                are stored in the object (`self.keys`, `self.data`).
    """
    self.flush()